most citedAdapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm

8 citations · 10 across the 5 of their papers we have counts for

collaborators

5 papers

quant-ph2020

Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions

Thomas Hubregtsen, Christoph Segler, Josef Pichlmeier +3

Quantum computers hold great promise for accelerating computationally challenging algorithms on noisy intermediate-scale quantum (NISQ) devices in the upcoming years. Much attentio…

cs.ET2019

Assessing Solution Quality of 3SAT on a Quantum Annealing Platform

Thomas Gabor, Sebastian Zielinski, Sebastian Feld +6

When solving propositional logic satisfiability (specifically 3SAT) using quantum annealing, we analyze the effect the difficulty of different instances of the problem has on the q…

cs.SE20198 cited

Adapting Quality Assurance to Adaptive Systems: The Scenario Coevolution Paradigm

Thomas Gabor, Marie Kiermeier, Andreas Sedlmeier +5

From formal and practical analysis, we identify new challenges that self-adaptive systems pose to the process of quality assurance. When tackling these, the effort spent on various…

cs.AI20192 cited

Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies

Thomy Phan, Kyrill Schmid, Lenz Belzner +3

Decision making in multi-agent systems (MAS) is a great challenge due to enormous state and joint action spaces as well as uncertainty, making centralized control generally infeasi…

cs.LG2019

Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning

Andreas Sedlmeier, Thomas Gabor, Thomy Phan +2

We consider the problem of detecting out-of-distribution (OOD) samples in deep reinforcement learning. In a value based reinforcement learning setting, we propose to use uncertaint…